輝度統計

下図の２値画像を２値ブローブ解析して得られたリージョンにより処理対象画像の輝度統計を算出する処理です。

【処理対象】	【２値画像】

【リージョン】

処理時間:

    0.082 msec : FVCL.Blob.CFvBlob
    0.021 msec : FVCL.Blob.GetList
    0.005 msec : FVCL.Blob.GetSortList
    0.121 msec : FVCL.Measure.CFvStatisticsEx

処理結果:

No.  ,Count,      Sum,     Mean,      Min,      Max,    Sigma,      Var
[000]  2320,   349602,  150.691,   75.000,  237.000,   23.030,  530.361
[001]  1967,   203810,  103.615,   75.000,  222.000,   16.912,  285.999
[002]  1874,   221519,  118.207,   75.000,  238.000,   19.180,  367.871
[003]  2341,   308658,  131.849,   75.000,  220.000,   24.010,  576.476
[004]   420,    61335,  146.036,   75.000,  236.000,   23.031,  530.430
[005]   428,    63780,  149.019,   75.000,  249.000,   29.004,  841.210
[006]   426,    53726,  126.117,   75.000,  205.000,   17.404,  302.906

サンプルコード:

//  $Revision: 1.5 $
void StatisticsEx()
{
#ifdef _UNICODE
    typedef std::wstring    _TSTRING;
#else
    typedef std::string     _TSTRING;
#endif

    FVCL::Data::CFvImage    src(_T("./key_UC8_M.png"));
    FVCL::Data::CFvImage    bin(_T("./key_BIN_M.png"));
    FVCL::CFvTimeCounter    watch;
    double                  msec;
    std::map<_TSTRING, double>  times;

    // --------------------------------------------------
    // ２値ブローブ解析.
    FVCL::Blob::CFvBlobResult   result;
    {
        watch.Start();
        FVCL::Blob::CFvBlob         parser(&bin, &result);
        FVCL::Blob::CFvBlobParam&   param = parser.GetParam();
        param.SetColorMode( FVCL::Blob::ObjectColor::WhiteFG );
        parser.Execute();
        msec = watch.Stop();
        times[_T("FVCL.Blob.CFvBlob")] = msec;
    }

    // --------------------------------------------------
    // ２値ブローブ解析結果のフィルタリングとソート.
    FHANDLE                 hresult = result.GetHandle();
    FVCL::Blob::CFvBlobData data(hresult, 0);
    FVCL::CFvBuffer<UINT>   blob_numbers;
    {
        watch.Start();
        FVCL::CFvArray<FVCL::Blob::CFvBlobFilterRange> filters;
        filters.resize(1);
        filters[0].SetType(FVCL::Blob::FeatureType::AREA);
        filters[0].SetMin(100);
        filters[0].SetMax(FVCL::TypeRange::INT::Upper);
        FVCL::Blob::GetList(hresult, filters, &blob_numbers);
        msec = watch.Stop();
        times[_T("FVCL.Blob.GetList")] = msec;
    }
    {
        watch.Start();
        FVCL::Blob::GetSortList(hresult, FVCL::Blob::FeatureType::RECT1AREA, 1, &blob_numbers);
        msec = watch.Stop();
        times[_T("FVCL.Blob.GetSortList")] = msec;
    }

    // --------------------------------------------------
    // 計測.
    {
        FVCL::Measure::CFvStatisticsEx  parser(&src, 0);

        _tprintf(_T("%-5s,%5s,%9s,%9s,%9s,%9s,%9s,%9s\n"),
            _T("No."), _T("Count"), _T("Sum"), _T("Mean"), _T("Min"), _T("Max"), _T("Sigma"), _T("Var"));
        msec = 0;
        for(UINT i = 0 ; i<blob_numbers.size() ; i++)
        {
            FVCL::Statistics        stat;
            FVCL::Data::CFvRegion   region;

            watch.Start();
            {
                data.SetBlobNo( blob_numbers[i] );
                data.GetRegion(&region);

                parser.Execute( region, FVCL::OriginMode::Image );
                stat = parser.Result;
            }
            msec += watch.Stop();

            _tprintf(_T("[%03d] %5d,%9.0f,%9.3f,%9.3f,%9.3f,%9.3f,%9.3f\n"),
                i, stat.Count, stat.Sum, stat.Mean, stat.Min, stat.Max, stat.Sigma, stat.Variance);
        }
        times[_T("FVCL.Measure.CFvStatisticsEx")] = msec;
    }

    // --------------------------------------------------
    _tprintf(_T("\n"));
    for(std::map<_TSTRING, double>::iterator iter = times.begin() ; iter != times.end() ; iter++)
    {
        _tprintf(_T("%9.3f msec : %s\n"), iter->second, iter->first.c_str());
    }
}